Introduction to probability and data

hi this is debrupa nandi today is my second day with git

studies

observational - 1. collect data in a way that does not directly interfere with how the data arise(“observe”) 2. only establish an association 3. retrospective uses past data 4. prospective - data are collected throughout the study

experiment- randomly assign subjects to treatments

Confounding variables - extraneous variables that affect both the explanatory and the response variable and that make it seem like there is a relationship between them

Correlation does not imply causation Observational staements help make us make correlation statements Experiments help us infer causation

In statistics, a confounder is a variable that influences both the dependent variable and independent variable, causing a spurious association. Confounding is a causal concept, and as such, cannot be described in terms of correlations or associations.

library(dplyr)
library(ggplot2)
library(statsr)
data(arbuthnot)
View(arbuthnot)

library(dplyr)
library(ggplot2)
library(statsr)

data(present)
View(present)

present <- present %>%
  mutate(total = boys + girls)
prop_boys <- present$boys/present$total
present <- present %>%
  mutate(prop_boys)
ggplot(data = present, aes(x = year, y = prop_boys)) +
  geom_line() + geom_point() + geom_smooth()

present <- present %>%
  mutate(more_boys = present$boys > present$girls)
View(present)
present <- present %>%
  mutate(prop_boy_girl = boys / girls)
View(present)
ggplot(data = present, aes(x = year, y = prop_boy_girl)) +
  geom_line() + geom_point() + geom_smooth()

present <- present %>%
arrange(desc(total))

Robust statistics are statistics with good performance for data drawn from a wide range of probability distributions, especially for distributions that are not normal. Robust statistical methods have been developed for many common problems, such as estimating location, scale, and regression parameters.

The response variable is the focus of a question in a study or experiment. An explanatory variable is one that explains changes in that variable. It can be anything that might affect the response variable. Let’s say you’re trying to figure out if chemo or anti-estrogen treatment is better procedure for breast cancer patients. The question is: which procedure prolongs life more? And so survival time is the response variable. The type of therapy given is the explanatory variable; it may or may not affect the response variable. In this example, we have only one explanatory variable: type of treatment. In real life you would have several more explanatory variables, including: age, health, weight and other lifestyle factors.